Efficient MultiClass Maximum Margin Clustering Bin Zhao zhaobinhere@hotmail.com Fei Wang feiwang03@mails.tsinghua.edu.cn Changshui Zhang zcs@mail.tsinghua.edu.cn State Key Laboratory of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China Abstract This paper presents
Learning Structural SVMs with Latent Variables Chun-Nam John Yu cnyu@cs.cornell.edu Thorsten Joachims tj@cs.cornell.edu Department of Computer Science, Cornell University, Ithaca, NY 14850 USA Abstract We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of applica- tion problems, with an opt
A New Analysis of Co-Training Wei Wang wangw@lamda.nju.edu.cn Zhi-Hua Zhou zhouzh@lamda.nju.edu.cn National Key Laboratory for Novel Software Technology, Nanjing University, China Abstract In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training pro- cess is viewed as a combinative lab
Large Graph Construction for Scalable Semi-Supervised Learning Wei Liu wliu@ee.columbia.edu Junfeng He jh2700@columbia.edu Shih-Fu Chang sfchang@ee.columbia.edu Department of Electrical Engineering, Columbia University, New York, NY 10027, USA Abstract In this paper, we address the scalability issue plaguing graph-based semi-supervised learn- ing via a small number of anchor points which adequatel
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